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Bank lending and environmental quality in Gulf Cooperation Council countries
To achieve economies with net-zero carbon emissions, it is essential to develop a robust green financial intermediary channel. This study seeks empirical evidence on how domestic bank lending to sovereign and private sectors in Gulf Cooperation Council (GCC) countries impacts carbon dioxide and greenhouse gas emissions. We employ PMG-ARDL model to panel data comprising six countries in GCC over twenty years for carbon dioxide emissions and nineteen years for greenhouse gas emissions. Our findings reveal a long-term positive impact of both bank lending variables on carbon dioxide and greenhouse gas emissions. In addition, lending to the government shows a negative short-term effect on greenhouse gas emissions. The cross-country results demonstrate the presence of a long-run effect of explanatory variables on both types of emissions, except for greenhouse gas in Saudi Arabia. The sort-term impact of the explanatory variables on carbon dioxide and greenhouse gas emissions is quite diverse. Not only do these effects differ across countries, but some variables have opposing effects on the two types of emissions within a single country. The findings of this study present a new perspective for GCC economies: neglecting total greenhouse gas emissions and concentrating solely on carbon dioxide emissions means missing critical information for devising effective strategies to combat threats of environmental degradation and achieve net-zero goals.
Mechanisms of electrochemical hydrogenation of aromatic compound mixtures over a bimetallic PtRu catalyst
Efficient electrochemical hydrogenation (ECH) of organic compounds is essential for sustainability, promoting chemical feedstock circularity and synthetic fuel production. This study investigates the ECH of benzoic acid, phenol, guaiacol, and their mixtures, key components in upgradeable oils, using a carbon-supported PtRu catalyst under varying initial concentrations, temperatures, and current densities. Phenol achieved the highest conversion (83.17%) with a 60% Faradaic efficiency (FE). In mixtures, benzoic acid + phenol yielded the best performance (64.19% conversion, 74% FE), indicating a synergistic effect. Notably, BA consistently exhibited 100% selectivity for cyclohexane carboxylic acid (CCA) across all conditions. Density functional theory (DFT) calculations revealed that parallel adsorption of BA on the cathode (−1.12 eV) is more stable than perpendicular positioning (-0.58 eV), explaining the high selectivity for CCA. These findings provide a foundation for future developments in ECH of real pyrolysis oil.
Machine learning map of climate policy literature reveals disparities between scientific attention, policy density, and emissions
Current climate mitigation policies are not sufficient to meet the Paris temperature target, and ramping up efforts will require rapid learning from the scientific literature on climate policies. This literature is vast and widely dispersed, as well as hard to define and categorise, hampering systematic efforts to learn from it. We use a machine learning pipeline using transformer-based language models to systematically map the relevant scientific literature on climate policies at scale and in real-time. Our “living systematic map” of climate policy research features a set of 84,990 papers, and classifies each of them by policy instrument type, sector, and geography. We explore how the distribution of these papers varies across countries, and compare this to the distribution of emissions and enacted climate policies. Results suggests a potential stark under-representation of industry sector policies, as well as diverging attention between science and policy with respect to economic and regulatory instruments.
Anion vacancies activate N2 to ammonia on Ba–Si orthosilicate oxynitride-hydride
Anion vacancies on metal oxide surfaces have been studied as either active sites or promoting sites in various chemical reactions involving oxidation/reduction processes. However, oxide materials rarely work effectively as catalysts in the absence of transition metal sites. Here we report a Ba–Si orthosilicate oxynitride–hydride as a transition-metal-free catalyst for efficient ammonia synthesis via an anion-vacancy–mediated mechanism. The facile desorption of H− and N3− anions plus the flexibility of the crystal structure can accommodate a high density of electrons at vacancy sites, where N2 can be captured and directly activated to ammonia through hydrogenation processes. The ammonia synthesis rates reach 40.1 mmol g−1 h−1 at 300 °C by loading ruthenium nanoparticles. Although not found to dissociate N2, Ru instead facilitates the formation of anion vacancies at the Ru–support interface. This demonstrates a new route for anion-vacancy–mediated heterogeneous catalysis.
First-principles and machine-learning approaches for interpreting and predicting the properties of MXenes
MXenes are a versatile family of 2D inorganic materials with applications in energy storage, shielding, sensing, and catalysis. This review highlights computational studies using density functional theory and machine-learning approaches to explore their structure (stacking, functionalization, doping), properties (electronic, mechanical, magnetic), and application potential. Key advances and challenges are critically examined, offering insights into applying computational research to transition these materials from the lab to practical use.
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